Affordable AI Solutions for Semiconductor Inspection and Defect Detection

Explore the challenges in semiconductor inspection and how Deep Block's AI technology offers cost-effective, efficient solutions for defect detection and failure analysis.

Challenges and Limitations of Current Semiconductor Inspection Equipment

Currently, the market for inspection equipment is, first and foremost, incredibly expensive.

For instance, KLA sells inspection equipment at prices approaching 10 million USD, while other companies, even at their most affordable, offer equipment that costs nearly 4 million USD per unit.

These companies typically utilize Deep Ultraviolet (DUV) light sources, and the inspection process often involves spinning the wafer while probing and conducting inspections.

This process is time-consuming, and with DUV light sources, one can only ascertain the presence of a defect.

As defects become smaller, they become increasingly difficult to detect, and even if they are found, identifying the type of defect and its specific shape remains a challenge.

In the front-end process alone, the shapes of gates are becoming increasingly complex (for example, with the introduction of Gate-All-Around (GAA) or RibbonFET structures).

The adoption of Extreme Ultraviolet (EUV) lithography has led to finer patterns, resulting in an increase in the number of defects while their sizes decrease.

However, optical inspection devices continue to rely heavily on DUV and dark field imaging techniques.

To accurately detect all random defects, it is generally necessary to use bright field inspection or E-beam inspection equipment manufactured by companies like Applied Materials.

However, compared to dark field imaging, bright field inspection equipment has a slower throughput.

At best, bright field inspection equipment can process only a few wafers per hour, and there are limitations in detecting all types of defects.

Moreover, the cost of such equipment is around 10 million USD, with KLA holding a near-monopoly in the market.

Naturally, such expensive and slow equipment is difficult for small companies or research institutions to purchase, and even if they do, it is impossible to detect all defects.

The development of inspection equipment requires advanced optical technology, but there are quite a few companies outside of inspection equipment manufacturers that excel in optics.

Microscopes as a Cost-Effective Alternative for Semiconductor Inspection and Research

For researchers and fab companies with limited financial resources, the most valuable tool for defect inspection and process analysis is the microscope. Whether optical or electron-based, microscopes provide an essential and cost-effective alternative to high-priced semiconductor inspection equipment. Failure analysis and process research often require detailed imaging, and while dedicated optical inspection systems can cost millions of dollars, microscopes—especially advanced electron microscopes—offer comparable or even superior imaging capabilities at a fraction of the price.

For instance, Carl Zeiss has developed Multi-Beam Scanning Electron Microscopes (Multi-SEMs) that dramatically improve imaging speed compared to traditional SEMs. A Carl Zeiss Multi-SEM can be acquired for approximately 2 million USD, whereas KLA’s optical inspection equipment, which is priced at around 10 million USD, operates at a slower imaging speed. Despite being significantly cheaper, Multi-SEMs provide high-throughput imaging, making them a practical choice for many researchers and fab engineers.

Even manufacturers of basic optical microscopes have developed fast-scanning technologies that surpass traditional wafer inspection methods. Companies like Carl Zeiss and Philips have pioneered rapid slide-scanning techniques in the medical field, utilizing advanced scanning technologies that can process images faster than conventional wafer-spinning techniques used in optical inspection systems. These advancements indicate that rapid imaging solutions exist outside of the traditional semiconductor inspection industry, offering viable alternatives for those constrained by budget limitations.

The Practicality of Microscopes for Researchers and Small Fab Facilities

While it is true that basic bright field imaging with visible spectrum light sources has limitations in detecting extremely small defects—particularly compared to dedicated semiconductor inspection equipment—this constraint is not always a critical issue for certain researchers and fab engineers.

1. Fab Equipment Limitations – Many small research fabs and legacy semiconductor manufacturers do not possess the capability to produce ultra-fine patterns. In such cases, defect sizes are naturally larger, and minor defects do not always pose significant problems to device performance. As a result, extreme sensitivity in defect detection is not always necessary.

2. Financial Constraints – Purchasing high-end optical inspection equipment is simply not an option for many research institutions, small fabs, or those working on niche semiconductor applications such as prototype MEMS (Micro-Electro-Mechanical Systems) or legacy semiconductor processes. Instead of investing in expensive inspection systems, they can allocate their budget toward high-resolution electron microscopes and cost-effective optical microscopes while leveraging advanced imaging techniques such as Deep Block to enhance their capabilities.

Ultimately, even when an expensive optical inspection tool can determine the presence of defects, it often cannot provide detailed information about their nature or root cause. In many cases, engineers and researchers must still rely on electron microscopes for high-resolution imaging to analyze defect morphology and determine their underlying causes. This highlights the continued importance of electron microscopy and other affordable imaging techniques in semiconductor research and manufacturing.

The Value of Deep Block

Emerging semiconductor manufacturing nations, small research labs, and individual researchers are actively engaged in semiconductor fabrication and MEMS research. These institutions are particularly interested in advanced packaging technologies, cost-effective large-area image analysis, and automation using artificial intelligence.

Deep Block provides immense value in this domain. High-resolution imaging solutions, such as the Carl Zeiss Multi-Beam SEM (M-SEM), already exist, allowing researchers to capture large-scale microscopic images efficiently. Moreover, material scientists and engineers are well-versed in image stitching techniques and the software tools required to assemble large-area micrographs.

Our Proposed Workflow

Deep Block offers a streamlined approach for automating process research, failure analysis, and defect detection. The workflow consists of the following steps:

1. Micrograph Acquisition – Capture high-resolution images using an optical or electron microscope.
2. Image Stitching or Large-Area Image Processing – Combine multiple microscopic images into a single, large-format dataset.
3. Micrograph File Extraction in TIFF Format – Convert and export the large-area image into a standardized format for analysis.
4. Deep Block Analysis – Upload the image into Deep Block, where AI-powered algorithms automatically identify defects, recognize various structural elements (such as metals and circuit features like bump), and classify anomalies.

By implementing this workflow, users can automate process research, failure analysis, and root cause analysis while also streamlining pattern inspection.

Deep Block: Accessible and Flexible AI-Powered Inspection

Deep Block is currently available as an online free trial, and for users concerned about data privacy, it also offers on-premise workstation and server versions.

One of Deep Block’s major advantages is that users do not need any coding expertise to build and deploy computer vision models. Instead of purchasing multiple AI software solutions for different tasks, Deep Block enables users to automate a wide range of inspection and metrology processes with a single, versatile platform.

For example:

• In FEOL (Front-End-of-Line) processes, Deep Block can be used for defect detection and gate size measurement.
• In wafer-level packaging, Deep Block can train AI models to automate inspection tasks.
• The platform supports various imaging techniques, including electron microscopy, optical microscopy, and EMMI, making it adaptable to different semiconductor inspection workflows.

Below is a demonstration of how Deep Block enables the creation of an AI model for BEOL (Back-End-of-Line) defect detection:

Bringing Affordable AI Automation to Semiconductor Inspection

Deep Block empowers users to build, train, and deploy AI models for image analysis in a highly efficient and cost-effective manner. Whether operating in-house or in an on-premise environment, users can:

• Label training data for AI models
• Train and optimize AI models for microscopic image analysis
• Automate micrograph interpretation through an intuitive graphical interface

By leveraging Deep Block, semiconductor researchers and engineers can significantly reduce inspection costs while enhancing the accuracy and speed of defect detection and metrology. Instead of relying on traditional inspection equipment with limited automation capabilities, Deep Block provides a flexible, AI-powered alternative for modern semiconductor fabrication and failure analysis.